pyGPs is a Python project for Gaussian process (GP) regression and classification for machine learning.

pyGPs is an object-oriented implemetation of GP regression and classificaion additionally supporting useful routines for the practical use of GPs, such as cross validation functionalities for evaluation as well as basic routines for iterative restarts for the GP hyperparameter optimization.

Note, there is also a procedural implementation of GPs (pyGP_PR) which follows structure and functionality of the gpml matlab implementaion by Carl Edward Rasmussen and Hannes Nickisch (Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2013-01-21). This version can be downloaded via this link: https://github.com/marionmari/pyGP_PR/archive/v1.1.tar.gz.

Future extensions will be designed for pyGPs. pyGP_PR will be maintained as it is now.

Changes to previous version:

Changelog pyGPs v1.3.1

November 25th 2014

structural updates:

full inline documentation with input parameter and output specified

check for the inputs and provide diagnostic messages for some inputs

consistant naming in inline and online documentation

string representation for dnlZStruct and postStruct. Now you can do sth like:

nlZ, dnlZ, post = model.getPosterior(x,y)

print post

instead of a python object, we provide now a more informative
description.